Transcriptional dynamics orchestrating the development and integration of neurons born in the adult hippocampus

The adult hippocampus generates new granule cells (aGCs) with functional capabilities that convey unique forms of plasticity to the preexisting circuits. While early differentiation of adult radial glia-like cells (RGLs) has been studied extensively, the molecular mechanisms guiding the maturation of postmitotic neurons remain unknown. Here, we used a precise birthdating strategy to study aGC differentiation using single-nuclei RNA sequencing. Transcriptional profiling revealed a continuous trajectory from RGLs to mature aGCs, with multiple immature stages bearing increasing levels of effector genes supporting growth, excitability, and synaptogenesis. Analysis of differential gene expression, pseudo-time trajectory, and transcription factors (TFs) revealed critical transitions defining four cellular states: quiescent RGLs, proliferative progenitors, immature aGCs, and mature aGCs. Becoming mature aGCs involved a transcriptional switch that shuts down pathways promoting cell growth, such SoxC TFs, to activate programs that likely control neuronal homeostasis. aGCs overexpressing Sox4 or Sox11 remained immature. Our results unveil precise molecular mechanisms driving adult RGLs through the pathway of neuronal differentiation.


Figure S1. Experimental pipeline and quality control for dataset 1. (A)
Ascl1 CreERT2 ;CAG floxStop-Sun1/sfGFP mice received tamoxifen (TAM) injections to label nuclei from the progeny of RGLs and NPCs.Dentate giri were microdissected at the indicated timepoints (cohorts).Each cohort of GFP + nuclei was FACS-purified and processed separately using 10x      S3. (C) Top GO biological processes for the enrichment analysis of DEGs in the transition from NB1 to NB2.FDR cutoff = 0.05 (using ShinyGO 0.77).All data in the figure correspond to dataset 1.

Figure S6. Marker genes for GCimm. (A,B)
Heatmaps showing the row-wise normalized expression of marker genes for GCimm1 and GCimm2 across clusters (top 50 genes).Marker genes were selected as described in the Methods section.Pseudocolor scales on the right denote mean expression level.Data correspond to dataset 1.  S3.  S3.
Table S4.DATASET 1. Ion channels, neurotransmitter receptors, and molecules involved in synaptic transmission, corresponding to Figure 5E.
Chromium technology for cDNA library preparation and sequencing.Markers were studied by fluorescence in situ hybridization.(B) Nuclei composition for each cohort, indicating mice number and sex (F, females; M, males), number of FACS-sorted, and analyzed nuclei.(C) FACS sorting of Ruby + /GFP + nuclei.Scatter plots of ruby dye intensity vs. log(GFP intensity) for purified nuclei isolated at w1, w2, w4 and w8.Green squares highlight the GFP + /Ruby + population, with the number indicating % GFP + nuclei in the total population.(D) Confocal images of FACS-purified Ruby + nuclei exhibiting GFP anchored to the nuclear membrane.Scale bars: 50 µm (upper panels) and 10 µm (lower panels).(E) Quality measurements of snRNA-seq libraries.Scatter plot depicting the number of genes (features) vs. the number of mRNA molecules (counts) per nuclei for each timepoint.snRNA-seq detected similar number of genes and transcript molecules across cohorts.(F) Quality measurements of snRNA-seq for each cluster.Box plots depict the number of counts, features, and their ratio.snRNA-seq detected similar number of genes and transcript molecules across clusters.(G) Contribution of timed cohorts to cluster composition.

Figure S2 .
Figure S2.Quality control and cluster distribution for dataset 2. (A) Nuclei composition for each cohort, indicating mice number and sex (F, females; M, males), number of FACS-sorted, and analyzed nuclei.(B) Quality measurements of snRNA-seq libraries.Scatter plot depicting the number of genes (Features) vs. the number of mRNA molecules (counts) per nuclei for each timepoint.snRNA-seq detected similar number of genes and transcript molecules across cohorts.(C) Quality measurements of snRNA-seq for each cluster.Box plots depict the number of counts, features, and their ratio.snRNA-seq detected similar number of genes and transcript molecules across clusters.(D) Nuclei distribution in all clusters for each neuronal cohort.(E) Contribution of timed cohorts to cluster composition.(F) Bar chart indicating the number of DEGs between adjacent cluster transitions unique for dataset 1 (pink), dataset 2 (turquoise) and their intersection (blue).

Figure S3 .
Figure S3.Cluster identity and transitions in dataset 2. (A) mKNN graph displaying cluster identity.(B) Progression of each cohort and their localization over the mKNN graph.Nuclei density is indicated by the yellow (low) to red (high) gradient.(C) Violin plot showing the expression level of canonical marker genes for the defined clusters.Note that most identified clusters are conserved with dataset 1, highlighting the reproducibility between experiments.

Figure S4 .
Figure S4.Pseudotime and density profiles are conserved between datasets.(A) DEGs between adjacent cluster transitions indicated on top (dataset 1; DEGs listed in TableS3).Additional comparisons (non-adjacent) are shown on the right.Up-and downregulated genes are shown in red and blue.(B) Density distribution of all nuclei along the pseudotime progression for each cohort, comparing datasets 1 and 2. The four cellular states are conserved: 1 st RGL; 2 nd NPC; 3 rd NB1-GCimm1-GCimm2; 4 th GCyoung-GCmat1.Dashed lines depict major transitions along development.Color codes below denote cluster identity (C) Heatmap displaying the row-wise normalized expression of transcription factors specific for RGLs, depicting additional transcripts to those shown in Fig. 3C.Maximum expression (MaxExp, colored scale on the right) is shown on the left.Pseudocolor scale on the right denotes mean expression level.Color-coded clusters are indicated below.

Figure S5 .
Figure S5.Molecular signatures of early stages of adult neurogenesis.(A) Heatmap showing the mean of row-wise normalized expression of canonical cell-cycle markers across clusters.Pseudocolor scale on the right denotes mean expression level.(B) between NB1 and NB2.Left panel: volcano plot showing differential expression analysis (FC  1.5 or  -1.5 and FDR  0.05) with relevant gene examples.Right panel: heatmap showing the row-wise normalized expression of up-(red) and downregulated DEGs (blue) across clusters (top 50 genes per transition).Pseudocolor scale on the right denotes mean expression level.DEGs are listed in Table

Figure S7 .
Figure S7.Switch in biological processes during the exit from the immature state.(A, B) GO biological processes are shown for the enrichment analysis of the top-50 DEGs in the transition from GCimm2 to GCyoung.FDR cutoff = 0.05 (using ShinyGO 0.77).(C, D) Violin plots showing the mean expression levels of DEGs for downregulated (C) and upregulated GO terms (D) for the defined clusters.All data in the figure correspond to dataset 1. DEGs are listed in TableS3.

Figure S8 .
Figure S8.GCyoung and GCmat1 displays subtle transcriptomic differences and share nuclei with ventral signature.(A) Volcano plot showing differential expression analysis (FC  1.5 or  -1.5 and FDR  0.05) displaying relevant gene examples.All DEGs are listed in TableS3.

( B )
Violin plots showing the expression level of upregulated DEGs of the GCyoung to GCmat1 transition.All data in the figure correspond to dataset 1.

Figure S9 .
Figure S9.Regulon expression for the different cellular states.(A) Regulon activity analysisbased on the expression levels of TF targets comparing the 1 st with the 2 nd and 2 nd to the 3 rd states.Each regulon contains targets genes identified by SCENIC according to the conserved DNA binding motifs in regulatory regions.Regulon sizes are shown in parentheses.Extended regulons include targets inferred by binding motif similarity.Regulon compositions are listed in TableS5.RSS: Regulon specificity score 46 .The z score color scale depicts standardized expression activity values.(B, C) Violin plots displaying the mean expression of transcripts that compose the indicated regulons.All data in the figure correspond to dataset 2.